scholarly journals Lung nodule modeling and detection for computerized image analysis of low dose CT imaging of the chest.

2009 ◽  
Author(s):  
Amal Farag
Keyword(s):  
Image Analysis ◽  
Low Dose ◽  
Lung Nodule ◽  
Ct Imaging ◽  
Computerized Image Analysis ◽  
Low Dose Ct

LUNGRADS AND UPDATE OF LUNG NODULES SCREENING BY LOW DOSE COMPUTED TOMOGRAPHY

2015 ◽  
pp. 12-19
Author(s):  
Thi Ngoc Ha Hoang ◽  
Trong Khoan Le
Keyword(s):  
Lung Cancer ◽  
Low Dose ◽  
False Negative ◽  
Lung Nodule ◽  
Lung Cancer Mortality ◽  
Smoking History ◽  
Low Dose Ct ◽  
History Of ◽  
Chest X Ray ◽  
Low Dose Computed Tomography

Background: A pulmonary nodule is defined as a rounded or irregular opacity, well or poorly defined, measuring up to 3 cm in diameter. Early detection the malignancy of nodules has a significant role in decreasing the mortality, increasing the survival time and consider as early diagnosis lung cancer. The main risk factors are those of current or former smokers, aged 55 to 74 years with a smoking history of at least 1 pack-day. Low dose CT: screening individuals with high risk of lung cancer by low dose CT scans could reduce lung cancer mortality by 20 percent compared to chest X-ray. Radiation dose has to maximum reduced but respect the rule of ALARA (As Low as Resonably Archivable). LungRADS 2014: Classification of American College of Radiology, LungRADS, is a newly application but showed many advantages in comparison with others classification such as increasing positive predict value (PPV), no result of false negative and cost effectiveness. Key words: LungRADS, screening lung nodule, low dose CT, lung cancer

Detecting airway remodeling in COPD and emphysema using low-dose CT imaging

2012 ◽  
Author(s):  
R. Rudyanto ◽  
M. Ceresa ◽  
A. Muñoz-Barrutia ◽  
C. Ortiz-de-Solorzano
Keyword(s):  
Low Dose ◽  
Airway Remodeling ◽  
Ct Imaging ◽  
Low Dose Ct

CLEAR: Comprehensive Learning Enabled Adversarial Reconstruction for Subtle Structure Enhanced Low-Dose CT Imaging

2021 ◽  
pp. 1-1
Author(s):  
Yikun Zhang ◽  
Dianlin Hu ◽  
Qianlong Zhao ◽  
Guotao Quan ◽  
Jin Liu ◽  
...  
Keyword(s):  
Low Dose ◽  
Ct Imaging ◽  
Low Dose Ct

Deep iterative reconstruction estimation (DIRE): approximate iterative reconstruction estimation for low dose CT imaging

2019 ◽  
Vol 64 (13) ◽  
pp. 135007 ◽  
Author(s):  
Jin Liu ◽  
Yi Zhang ◽  
Qianlong Zhao ◽  
Tianling Lv ◽  
Weiwen Wu ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Low Dose ◽  
Ct Imaging ◽  
Low Dose Ct

Discriminative feature representation: an effective postprocessing solution to low dose CT imaging

2017 ◽  
Vol 62 (6) ◽  
pp. 2103-2131 ◽  
Author(s):  
Yang Chen ◽  
Jin Liu ◽  
Yining Hu ◽  
Jian Yang ◽  
Luyao Shi ◽  
...  
Keyword(s):  
Low Dose ◽  
Ct Imaging ◽  
Feature Representation ◽  
Low Dose Ct ◽  
Discriminative Feature

DaNet: dose-aware network embedded with dose-level estimation for low-dose CT imaging

2020 ◽  
Author(s):  
Zhenxing Huang ◽  
Zixiang Chen ◽  
Jincai Chen ◽  
Ping Lu ◽  
Guotao Quan ◽  
...  
Keyword(s):  
Dose Level ◽  
Low Dose ◽  
Ct Imaging ◽  
Low Dose Ct

Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study

2021 ◽  
Author(s):  
ryoji mikayama ◽  
Takashi Shirasaka ◽  
Tsukasa Kojima ◽  
Yuki Sakai ◽  
Hidetake Yabuuchi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Iterative Reconstruction ◽  
Measurement Accuracy ◽  
Low Dose ◽  
Lung Nodule ◽  
Ground Glass ◽  
Volumetric Measurement ◽  
Low Dose Ct ◽  
Nodule Volume ◽  
Ground Glass Nodules

Objectives The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric accuracy among deep-learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and hybrid iterative reconstruction (HIR) at an ultra-low-dose setting. Methods Artificial ground-glass nodules (6 mm and 10 mm diameters, −660 HU) placed at the lung-apex and the middle-lung field in chest phantom were scanned by 320-row CT with the ultra-low-dose setting of 6.3 mAs. Each scan data set was reconstructed by DLR, MBIR, and HIR. The volumes of nodules were measured semi-automatically, and the absolute percent volumetric error (APEvol) was calculated. The APEvol provided by each reconstruction were compared by the Tukey-Kramer method. Inter- and intraobserver variabilities were evaluated by a Bland-Altman analysis with limits of agreements. Results DLR provided a lower APEvol compared to MBIR and HIR. The APEvol of DLR (1.36%) was significantly lower than those of the HIR (8.01%, p = 0.0022) and MBIR (7.30%, p = 0.0053) on a 10-mm-diameter middle-lung nodule. DLR showed narrower limits of agreement compared to MBIR and HIR in the inter- and intraobserver agreement of the volumetric measurement. Conclusions DLR showed higher accuracy compared to MBIR and HIR for the volumetric measurement of artificial ground-glass nodules by ultra-low-dose CT. Advances in knowledge DLR with ultra-low-dose setting allows a reduction of dose exposure, maintaining accuracy for the volumetry of lung nodule, especially in patients which deserve a long-term follow-up.

Deep Residual Convolutional Sparse Coding Networks for Low Dose CT Imaging

2021 ◽  
Author(s):  
Jin Liu ◽  
Zhenyu Xia ◽  
Yanqin Kang ◽  
Jun Qiang
Keyword(s):  
Sparse Coding ◽  
Low Dose ◽  
Ct Imaging ◽  
Low Dose Ct

scholarly journals Automated Lung Nodule Detection at Low-Dose CT: Preliminary Experience

2003 ◽  
Vol 4 (4) ◽  
pp. 211 ◽  
Author(s):  
Jin Mo Goo ◽  
Jeong Won Lee ◽  
Hyun Ju Lee ◽  
Seunghwan Kim ◽  
Jong Hyo Kim ◽  
...  
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Low Dose Ct ◽  
Lung Nodule Detection ◽  
Nodule Detection
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